Human detection can be seen as part of a bigger object detection problem. For instance, authors of [1]} suggested to combine a YOLOv3-based model with DeepSORT. Their algorithm managed to reach a mAP of 84.6% when trained and evaluated on the Open Image Dataset.
Several other works focused on object detection in aerial images [2]}–[3]}. They relied on the VisDrone2019 dataset for training and testing, but using different machine learning models. For instance, authors of
[2]} trained a CenterNet algorithm with a large input image resolution (2048 x 2048) in order to avoid the loss of discriminatory features for small objects. Their model reached mAP of 65% on the validation subset on human classes, i.e., people and pedestrian. Also, the authors of [5]} proposed a novel network structure called PENet (Points Estimated Network) that achieved 41% mAP on the validation subset on human classes, while
authors in [6]} combined Cascaded R-CNN with CenterNet through weighted box ensemble to design a new network called SyNet. Their proposed model detected humans with mAP of 43%. In contrast, Yu et al. proposed in [3]} an algorithm based on Faster R-CNN, called DSHNet. Between people and pedestrian classes, their model recorded a mAP of only 19.5%.
The aforementioned works demonstrate the difficulty to efficiently detect humans through aerial imagery. Indeed,
VisDrone2019, amongst only few other UAV-based image datasets, offers different perspectives, scales, and angles of capture, which complicates the detection algorithm generalization.
